evaluation of an industrial microgrid using ......u.p.b. sci. bull., series c, vol. 79, iss. 4, 2017...

U.P.B. Sci. Bull., Series C, Vol. 79, Iss. 4, 2017 ISSN 2286-3540

EVALUATION OF AN INDUSTRIAL MICROGRID USING

HOMER SOFTWARE

Andrei HORHOIANU1, Mircea EREMIA

2

The paper highlights the technical, economic and environmental aspects

related to the development of a microgrid at an industrial oil field level. Microgrid

simulation based on HOMER software has two main objectives. The first objective is

to determine the feasibility of the microgrid. HOMER considers the microgrid being feasible if it is able to meet the electricity and heat demand, as well as other

restrictions imposed by the user. The second goal is to estimate the life cycle cost of

the microgrid, represented by the installation and lifetime operating costs. The

availability of renewable energy resources strongly affects the behavior and

financial aspects of microgrids based on such energies, because the amount of

energy and generating duration are two functions strictly dependent upon the

availability of primary energy resources.

Keywords: HOMER, microgrid, renewable energy, load demand

1. Introduction

The technical and economic analysis of a microgrid is a difficult process

due to the number of design options and the uncertainty of key parameters, such

as demand profiles of thermal or electrical loads or fuel prices. Moreover,

renewables, by their intermittent nature and non-dispatchable seasonal production,

add additional complexity. To overcome all challenges mentioned above, a series

of software applications such as HOMER, RETScreen or DER-CAM can be used

for the technical and economic analyzes. In this paper, we opted to use HOMER

(Hybrid Optimization Model for Energy Resources) as it allows the user to carry

out the simulation, optimization and sensitivity analysis of a microgrid model. To

determine the microgrid’s feasibility and life cycle cost, HOMER examines, in the

simulation step, its hourly performances for the duration of one year. In the

optimization phase, HOMER simulates a series of possible configurations for the

microgrid, in order to determine the solution that satisfies all technical restrictions

at a minimum life cycle cost. In the sensitivity analysis stage, HOMER performs a

series of optimizations to smooth out uncertainties or changes in the assumptions

considered. The optimization process determines the optimal values of design

variables such as type, size and number of microgrid components, while the

1 PhD student, Dept. of Electrical Power Systems, University POLITEHNICA of Bucharest,

Romania, e-mail: [email protected] 2 Professor Emeritus, Dept. of Electrical Power Systems, University POLITEHNICA of Bucharest,

Romania, e-mail: [email protected]

194 Andrei Horhoianu, Mircea Eremia

sensitivity analysis assesses the effects of uncertainties or changes on microgrid

parameters such as average wind speed or the future fuel price. To limit the

complexity of input data and allow for a fast and practical calculation, the

simulation logic used by HOMER is less detailed than other simulation software

such as DesignPro or Hybrid2 [1] used in technical and economic analysis of

microgrids. On the other hand, HOMER is more detailed and advanced than

software such as RETScreen, which does not allow time series simulations [2].

2. Microgrid characteristics

To highlight the technical, economic and environmental aspects of an

industrial microgrid, we consider a microgrid developed on an existing oil field

site. As shown in Fig. 1, it is assumed that the microgrid is interconnected to a

public distribution network (20 kV) and it includes a series of production

equipment and distributed energy sources for efficient and safe operation of the

oil field resources.

Fig. 1. The microgrid architecture analyzed using HOMER

In terms of energy resources, required for exploitation of the oil field, the

microgrid includes two cogeneration units with gas turbines, one high capacity

boiler, two wind turbines and a photovoltaic system with a battery string for

Evaluation of an industrial microgrid using HOMER software 195

ensuring power supply of critical equipment. The optimum capacity of each

distributed generation unit is determined in the following sections using HOMER

software.

3. Microgrid components modeling

In HOMER simulation, the microgrid must include at least one electrical

and/or one thermal power source, and one destination of the generated energy

(e.g. electrical and/or thermal loads) or it must have the possibility of selling the

generated energy to the network. In the following sections, we present the way

microgrid components are modeled, using HOMER, as well as the technical and

economic aspects related to their common operation.

A. Load modeling

HOMER defines the term load, as an electrical or thermal energy demand.

The microgrid under review encompasses a number of electrical and thermal loads

spread over a relatively small area ( 50 𝑘𝑚2) at all levels of oil production within

the oil field. To simulate all these loads, HOMER requires the hourly values of

electricity demand registered at point of common coupling (PCC) as well as the

hourly values of thermal demand registered at the oil deposit and gathering and

treatment station.

Considering typical demand profiles for electricity and thermal usage

within an oil field from the North-East of Romania, we have set out in HOMER

the daily profiles of the electrical (Fig. 2.a) and thermal loads (Fig. 2 b). These

load profiles are dictated by the technological processes occurring at the oil field

under review.

a) b)

Fig. 2. Daily load profiles: (a) electrical (b) thermal

Analyzing the load profiles highlighted in Fig. 2 above, we can conclude

that at the point of common coupling, the total electrical demand of the microgrid


has a daily average of 22.000 kWh/day and records two peak times (8:00 – 1200

kW and 23:00 – 1500 kW), while the thermal demand of the oil deposit and

gathering and treatment station presents a daily average of 7200 kWh/day and a

peak of 500 kWh/day every 3 hours. Based on the specified daily load profiles,

HOMER determines the monthly and annually load profiles of the microgrid

(Figs. 3 a and b).

a) b)

Fig. 3. Monthly and annual load profiles: (a) electrical (b) thermal

B. Energy resources modeling

In HOMER simulation, the term energy resource refers to any form of

energy used by the microgrid to generate electrical power or heat. The energy

resources available within the HOMER software include four forms of renewable

energy ( solar, wind hydro and biomass) as well as fossil fuel based systems.

Renewable energy resources vary greatly depending on geographic location. For

example: solar energy is strictly dependent on climate and latitude; wind power

varies depending on weather fronts and landscaping; hydropower depends on

landscaping topology and rain while the biomass resources depend on local

biological productivity. Moreover, in any geographical areas, each renewable

energy source presents seasonal or even hourly variations.

The availability of renewable energy resources strongly affects the

behavior and financial aspects of a production system based on such energies

because the amount of energy and production duration are two functions, strictly

dependent upon the availability of primary energy resources. Therefore, the


appropriate modeling of renewable energy is an important aspect of any program

of technical and economic analysis.

For modeling the photovoltaic system, HOMER requires the geographical

coordinates of the system. Therfore, it is considered that the analyzed microgrid is

developed at an industrial oil field located in Moinesti, in the north-west of

Romania. After selecting the geographic location, HOMER generates statistic data

regarding the available solar energy in the selected area, using NASA database

and an algorithm developed by Graham and Hollands [3]. The obtained statistics

are presented in Fig. 4.

Fig. 4. Average values of solar radiation in Moinesti, Romania

As seen in Fig. 4, the horizontal solar radiation poses to the area under

consideration, an annual average of 3.25 kWh/m2/day and an average annual

clarity index of 0.41. Clearness index, defined as a subunitary ratio between solar

radiation at the ground level and the solar radiation at the atmosphere level, is a

measure of atmosphere clarity. With regards to the photovoltaic system, this is

modeled in HOMER as a device that produces electricity directly proportional to

the incident solar radiation from its surface, and independently of temperature and

voltage to which it is exposed.

HOMER calculates the energy produced by the photovoltaic system, using

the following equation [6]:

𝑃𝑃𝑉 = 𝑓𝑃𝑉 ∙ 𝑌𝑃𝑉 ∙𝐼𝑇

𝐼𝑠 (1)

where: 𝑃𝑃𝑉 – power generated by the photovoltaic system (kW)

𝑓𝑃𝑉 – correction factor of the nominal capacity

𝑌𝑃𝑉 – nominal capacity of the photovoltaic system (kW)

𝐼𝑇 – global solar radiation incident on the system surface (𝑘𝑊/𝑚2)


𝐼𝑠 – standard amount of solar radiation used for specifying the nominal capacity of the PV system

(1 𝑘𝑊/𝑚2)

The nominal capacity of a photovoltaic system represents the power

generated under standard test conditions. In HOMER, the power generated by the

PV system is always specified as the nominal capacity. For every hour of the year,

HOMER calculates the global solar radiation, incident on the surface of the PV

system using HDKR model developed by Duffie and Beckmann [4]. This model

takes into account: the amount of solar radiation global incidents in an open area;

orientation of the PV system; geographic location, and time of the year and day.

The correction factor (𝑓𝑃𝑉) highlights the effects of dust, losses in cables,

temperature and other factors that may cause deviations on the power generated

by the photovoltaic system from the values recorded under standard test. HOMER

does not consider the effect of temperature on the power generated by the

photovoltaic system, but the correction factor can be reduced to take account of

this aspect.

In reality, the power generated by the photovoltaic system depends

strongly and nonlinearly on the voltage to which is exposed. The peak power

(voltage level at which maximum power is generated) depends on solar radiation

and temperature. If the photovoltaic system is connected directly to d.c. load or a

battery system, then the voltage differs from the maximum point and system

performance will suffer. To address this deficiency, a device called maximum

power point tracker, is installed between the photovoltaic system and the other

components so that the voltage to which the photovoltaic system is exposed,

coincide with maximum value. HOMER considers that the simulated photovoltaic

system has a maximum power point tracker by default. To carry out the economic

calculations with HOMER, the user will have to specify the cost of initial

investment ($), the replacement cost ($) and annual operating and maintenance

costs ($/year). By default, the replacement cost is considered equal to the cost of

initial investment.

To define the search space of the optimal solution, in terms of technical

and economic aspects, we consider as standard values the following parameters:

Initial investment cost for a 25 kW photovoltaic system - 75.000 $;

Replacemnt cost for a 25 kW photovoltaic system - 75.000 $

Annual operating and maintenance costs for a 25 kW photovoltaic

system - 250 $/year

Liftetime of 25 kW photovoltaic system - 25 years

Correction factor - 80%

Considering these span values, the average value of solar energy in the

selected geographic area, the load profiles and the features and restrictions on

other elements of the microgrid (detailed in the following sections), HOMER


generates a series of possible configurations to identify the optimal solution,

characterized by a minimum net present cost. The characteristics of the

photovoltaic system obtained for the optimal solution, are presented in Fig. 5.

Fig. 5. Photovoltaic system characteristics obtained in the optimal solution

With regards to simulation of wind turbine systems in HOMER, the first

step is to generate data on the wind energy, available in the selected geographical

location. This data relates to the wind speeds encountered by the turbine during a

year and can be specified by the user or automatically generated by HOMER

using nASA database. In addition, HOMER generates four additional statistic

parameters: Weibull form factor, autocorrelation factor, daily power pattern and

peak hourly wind speed value. The characteristics of the wind speed, obtained by

HOMER for the geographical location of our microgrid, are presented in Fig. 6

and 7.

Fig. 6. Average wind speed values Fig. 7. Variation with altitude of wind speed


Analysing the above figures, we can observe that the wind speed has an

average value of 4.9 m/s and a logarithmic profile with respect to the variation of

the altitude. The wind speed additional parameters generated by HOMER are

presented in Table 1. Table 1

Wind speed parameters generated by HOMER

Parameter Value

Weibull form factor 2

Autocorrelation factor 0.85

Daily load pattern 0.25

Peak hourly wind speed 15

The Weibull form factor is a measure of the wind speed distortion over the

year. The autocorrelation factor highlights the dependence of wind speed at a

certain hour on the wind speed recorded in the previous hour. The daily load

pattern and peak hourly wind speed indicate the magnitude and phase of the daily

wind speed model. The wind turbine is modeled in HOMER as a device that

converts the kinetic wind energy into electricity after a power curve defined as a

variation of the power generated by the wind velocity. To perform the

simulations, HOMER considers a standard air density of 1.225 𝑘𝑔/𝑚3.

For each hour, HOMER calculates the power generated by the wind

turbine, following a four-step process. Thus, in a first stage HOMER determines

the average wind speed for the time considered on the basis of characteristic data

previously generated for wind energy. In the second step, HOMER determines the

average wind speed at the turbine nacelle using a function of speed variation with

altitude (Fig. 9). In the third stage, based on the turbine power curve (Fig. 8)

HOMER determines the power generated by the wind turbine considering the air

standard density. In the final step, the power calculated in step 3 is multiplied with

the ratio between the current air density and standard density.

Fig. 8. Wind turbine power curve


To define the search space of the optimal solution, in terms of technical

and economic aspects, we consider as standard values the following parameters:

Initial investment cost for a 1500 kW wind turbine - 3 M $

Replacement cost for a 1500 kW wind turbine - 3 M $

Annual operating and maintenance costs - 0.03 M $/year

Lifetime of the wind turbine - 20 years

The nacelle height - 80 m

Considering these span values, the average value of wind energy in the

selected geographic area, the load profiles and the features and restrictions on

other elements of the microgrid (detailed in the following sections), HOMER

generates a series of possible configurations to identify the optimal solution,

characterized by a minimum net present cost. The characteristics of the wind

turbine obtained for the optimal solution, are presented in Fig. 9.

Fig. 9. Wind turbine characteristics obtained in the optimal solution

With regards to cogeneration plants, HOMER defines these as equipment

used for generating electricity and heat from fossil fuels or biomass. The main

physical parameters defined by HOMER user, refer to generated electricity

(minimum and maximum), equipment lifetime in hours of operation, type of fuel

used and consumption curve. To serve the electricity and heat demand of the oil

field, we consider that the microgrid encloses two cogeneration plants with a

capacity of 2000 kW each. The parameters specified in HOMER, are presented in

Table 2.


Table 2

Parameters of cogeneration plants

Cogeneration

plant Parameter Value

CG01, CG02

Capacity ( kW) 2000

Initial investment cost ($) 1.200.000

Replacement cost ($) 1.200.000

Annual operating and maintenance cost ($/year) 30.000

Minimum loading (%) 10

Lifetime (hours) 15.000.000

Thermal energy recovery ratio (%) 40

Minimum operating time ( minutes) 120

As shown in Table 2, the two cogeneration plants have same technical

characteristics and can operate alternately or simultaneously in order to satisfy the

electricity and heat demand. We consider that the natural gas extracted in the oil

field represents the fuel used by the cogeneration plants following the

consumption curve presented in Fig. 10.

Fig. 10. Fuel consumption curves of cogeneration plants

The properties of the gas produced within the oil field and used within the

cogeneration plants, are presented in Table 3. Table 3

Fuel parameters

Parameter Value

Higher heating value ( MJ/kg) 43.2

Density (kg/𝑚3) 820

Carbon content (%) 88

Sulf content (%) 0.33

HOMER defines the fuel consumption curve using the following equation [6]:

𝐹 = 𝐹0𝑌𝑔𝑒𝑛 + 𝐹1𝑃𝑔𝑒𝑛 (2)

where:


𝐹0 – consumption curve interception coefficient, in l/hr/kW

𝐹1 – consumption curve slope, in l/hr/kW

𝑌𝑔𝑒𝑛 – nominal capacity of the cogeneration unit, in kW

𝑃𝑔𝑒𝑛 – electrical energy produced by the cogeneration unit, in kW

The emissions of pollutants are also taken into consideration in the

microgrid simulation. The emissions values considered for each of the two

cogeneration plants, are presented in Table 4. Table 4

Pollutants emissions values

Pollutant Value

Carbon monoxide ( g/l) 6.5

Incompletely burned hydrocarbons (g/l) 0.72

Solide particles (g/l) 0.49

Sulfur based compounds (g/l) 2.2

Nitrogen oxides ( g/l) 58

Based the on economic parameters defined in Table 2, HOMER calculates

the fixed and marginal cost of generating energy. These values are then used in

the simulation phase to determine the optimal economic and technical solution.

HOMER calculates the fixed cost of producing energy using the following

equation [6]:

𝑐𝑔𝑒𝑛,𝑓𝑖𝑥 = 𝑐𝑜𝑚,𝑔𝑒𝑛 +𝐶𝑟𝑒𝑝,𝑔𝑒𝑛

𝑅𝑔𝑒𝑛+ 𝐹0𝑌𝑔𝑒𝑛 ∙ 𝑐𝑓𝑢𝑒𝑙,𝑒𝑓𝑓 (3)

where:

𝑐𝑜𝑚,𝑔𝑒𝑛 –operating and maintenance costs, in $/hour

𝐶𝑟𝑒𝑝,𝑔𝑒𝑛 – replacement cost, in $

𝑅𝑔𝑒𝑛 – lifetime, in hrs

𝐹0 – consumption curve interception coefficient, in L/hr/kW

𝑌𝑔𝑒𝑛 – nomial capacity of the cogeneration unit, in kW

𝑐𝑓𝑢𝑒𝑙,𝑒𝑓𝑓 –fuel cost (including penalties due to pulluants), in $/l

HOMER calculates the marginal cost of producing energy using the

equation [6]:

𝑐𝑔𝑒𝑛,𝑚𝑎𝑟 = 𝐹1 ∙ 𝑐𝑓𝑢𝑒𝑙,𝑒𝑓𝑓 (4)

where:

𝐹1 – consumption curve slope, in l/hr/kW

𝑐𝑓𝑢𝑒𝑙,𝑒𝑓𝑓 – fuel cost (including penalties due to pulluants), in $/l

Taking into account all the above, the HOMER generates a series of

possible configurations to identify the optimal solution, characterized by a net


present cost. The characteristics of the cogeneration plants for the optimal

solution, are presented in Fig. 11.

Fig. 11. Cogeneration plants characteristics obtained in the optimal solution

The battery system enclosed within the microgrid is modeled in HOMER

as a device capable of storing a certain amount of energy at a reasonable

efficiency with a number of restrictions in terms of duration of loading,

discharging and lifetime. It is assumed that the properties of the battery system,

remain constant throughout its lifetime and are not affected by external factors

such as temperature.

The main physical parameters of the battery system, specified by HOMER

user are: rated voltage, rated power, lifetime, initial charge status, minimum

charging status and efficiency. The values of these parameters for the battery

system enclosed within the microgrid are summarized in Table 5. Table 5

Battery system parameters

Parameter Value

Nominal voltage (V) 48

Nominal power (kWh) 70

Initial charging status (%) 100

Minimum charging status (%) 40

Efficiency (%) 64

Lifetime (years) 20

In order to calculate the maximum degree of loading or unloading of the

battery system, HOMER considers the battery kinetic model [5], wherein the

battery is regarded as a system of two containers, as shown in Fig. 12.


Fig. 12. Battery kinetic model

According to the kinetic model, some of the energy stored in the battery is

immediately available for loading / unloading and the rest depends on the nature

of chemical bonds in the battery energy. The conversion rate of this type of

energy depends on the "height" of the two tanks. In this model, the battery is

described using the following parameters:

maximum capacity - the total size of the two tanks

capacity rate - the ratio between the available energy the bound

energy

constant conversion rate - the dimension of pipeline between the

two tanks

The battery kinetic model explains the typical capacity curve (Fig. 13) for

a battery system modeled in HOMER. Therefore, for high discharge rates, the

tank is draining quickly the available energy and only a small fraction of the

bound energy can be transferred to the reservoir of available energy, before it is

discharged. At this point of time, the battery can no longer cope with the demand

and behave like a completely empty tank.

Fig. 13. Typical capacity curve for a US-250 battery (www.usbattery.com)

Simulating the battery as a system of two reservoirs, allows emphasizing

the following two specific aspects:

http://www.usbattery.com/


The battery cannot charge/discharge fast; a full charge requires an

infinite amount of time for a given load current, which tends to zero.

The battery’s ability to charge/discharge, depends not only on the

charging status but also in the history of recent charge/discharge

cycle.

Therefore, a charged battery to 80% state of charge, will face higher

discharge rates, compared to a battery with a quick discharge to 80% because it

has a higher level in the reservoir of available energy. HOMER allows modeling

of both aspects outlined above, using kinetic model.

To perform the economic calculation using HOMER, the user has to

specify the cost of the battery system, the replacement cost and the annual cost of

operation and maintenance. The costs considered for the battery system enclosed

within our microgrid are presented in Table 6. Table 6

Specific costs for a 70 kWh battery system

Cost Value

Initial investment cost ($) 3.500

Replacement cost ($) 3.500

Annual maintenance and operation cost ($/year) 20

Due to the fact that the battery system is modeled as a dispatchable

energy source, HOMER calculates both fixed marginal costs, in order to make the

necessary comparisons with the other dispatchable systems and to determine the

optimal solution. Unlike cogeneration systems, the battery system is available

immediately for electricity production and therefore it is considered that the fixed

cost of energy is zero. The marginal cost consists of the deterioration cost of the

battery system (cost / kWh of energy required to charge the battery system) and

the average cost of energy stored in the battery.

The deterioration cost of the battery system, is determined using equation

[6]:

𝑐𝑏𝑤 =𝐶𝑟𝑒𝑝,𝑏𝑎𝑡𝑡

𝑁𝑏𝑎𝑡𝑡𝑄𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒√𝜂𝑟𝑡 (5)

where:

𝐶𝑟𝑒𝑝,𝑏𝑎𝑡𝑡 – replacement cost of the battery system, in $

𝑁𝑏𝑎𝑡𝑡 – number of batteries enclosed in the system

𝑄𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒 – battery lifetime, in kWh

𝜂𝑟𝑡– efficiency of the battery system

The characteristics of the battery system obtained in the optimal solution,

are presented in Fig. 14.


Fig. 14. Battery system characteristics obtained in the optimal solution

4. Simulation economic results

In the following paragraphs we present the economic results for the

optimal microgrid architecture obtained using HOMER. The optimal solution is

characterized by a minimum lifecycle cost and includes the following

components: a photovoltaic system, two wind generators, two cogeneration units

and a battery system equipped with a reversible AC/DC converter. The life cycle

cost elements of the microgrid are graphically summarized in Table 7. Table 7

Life cycle cost elements

System/Equipment

Investment

Cost

[MM $]

Replacement

Cost

[MM $]

O&M

Cost

[MM $]

Fuel

Cost

[MM $]

Residual

Value

[MM $]

Total

[MM $]

Photovoltaic System PV01

45 0 2.1 0 0 47.1

Wind Turbine GE01

3 1 0.5 0 -0.6 3.9

Wind Turbine

GE02 3 1 0.5 0 -0.6 3.9

Cogeneration Unit

CG01 0.06 0 61 2.5 -0.01 63.55

Cogeneration Unit

CG02 0.06 0.01 61 2.5 -0.01 63.55

Distribution Network 0 0 6 0 0 6

Battery System 0.03 0.01 0.01 0 0 0.05

AC/DC Converter 0.01 0.01 0 0 0 0.02

Contingency 0 0 5 0 0 5

Microgrid 52.24 2.03 136.11 5 1.22 193.07


The life cycle cost of the equipment / system represents the present value

of all costs of installing and operating it for the entire life of the project

(considered 25) minus the present value of all revenue generated by the equipment

/ system throughout the life of the project. HOMER calculates the life cycle cost

of each microgrid component and also for the whole microgrid. The discount rate

used is 6%. To determine the life cycle cost, HOMER generates a cash flow

chart (Fig. 57) in which all the cash flows are costs and appear as negative

numbers, excluding the residual value, which appears at the end of the project.

Fig. 15. Microgrid cash flow

The residual value of the equipment / energy system is defined as the

remaining value of the equipment at the end of the project. HOMER considers a

linear depreciation of equipment, which means that residual value is directly

proportional to the remaining life. It is also assumed that the residual value

depends on the replacement cost and not on the cost of the initial investment.

HOMER calculates the residual value using the following equations [6]:

𝑆 = 𝐶𝑟𝑒𝑝 ∙𝑅𝑟𝑒𝑚

𝑅𝑐𝑜𝑚𝑝 (6)

where, 𝑅𝑟𝑒𝑚 = 𝑅𝑐𝑜𝑚𝑝 − (𝑅𝑝𝑟𝑜𝑗 − 𝑅𝑟𝑒𝑝) (7)

and, 𝑅𝑟𝑒𝑝 = 𝑅𝑐𝑜𝑚𝑝 ∙ 𝐼𝑁𝑇(𝑅𝑝𝑟𝑜𝑗

𝑅𝑐𝑜𝑚𝑝

) (8)

The terms of the above equations, have the following meaning:


S – residual value [$]

𝐶𝑟𝑒𝑝 – replacement cost [$]

𝑅𝑐𝑜𝑚𝑝 – equipment lifetime [years]

𝑅𝑝𝑟𝑜𝑗 – project life [years]

𝐼𝑁𝑇() – integer function

5. Pollutant emissions evaluation

The main systems enclosed within the microgrid that generate emissions

are the two cogeneration units (CG01 and CG02). The quantities and types of

emissions determined by HOMER for these systems, are presented in Table 8.

Table 8

Microgrid pollutant emissions

Pollutant Quantity

[kg/year]

Carbon dioxide 3559660

Carbon monoxide 4692

Unburned hydrocarbons 520

Particulate matter 354

Sulf dioxide 11009

Nitrogen oxides 45382

HOMER considers by default, a penalty cost of $10 / tonne for each type

of pollutant specified in Table 8. This cost penalty is reflected in the composition

of the costs of operation and maintenance (O & M) specified in Table 7.

6. Conclusion

HOMER is a software application that simulates all possible microgrid

configurations for which the user wants to analyze the technical and economic

aspects. Simulated systems can be sorted and filtered into the optimization phase

depending on criteria set by the user in order to determine the optimal solution.

Although fundamentally HOMER is an economic optimization software, it can be

used to minimize fuel consumption or sensitivities analysis such as wind speed,

sunlight, fuel cost, etc.

Implementation of a microgrid at an oil field is a difficult process due to

the number of design options and the uncertainty of key parameters, such as

thermal or electrical load profiles and fuel prices. Moreover, renewables by their

intermittent nature and nondispatchable seasonal production, add additional

complexity.

The particularity of the processes undertaken at an oil field impose the

entrainment of dispatchable power sources to ensure the flexibility required to


satisfy the electricity and heat demand. However, the dispatchable power sources

are generally based on fossil fuel and have a negative impact on the microgrid life

cycle cost. In addition, penalties related to air pollution significantly increase the

opreating and maintenance costs.

R E F E R E N C E S

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[2] RETScreen International http://www.retscreen.net [3] V. A. Graham and K. G. T. Hollands, “A method to generate synthetic hourly solar radiation

globally”, Solar Energy, Vol. 44, No. 6, pp. 333–341, 2012

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